code: def _train(
      self,
      model,
      optim,
      virtual_pipeline_model_parallel_size,
      pipeline_model_parallel_size,
      async_comm,
  ):
      args = global_vars.get_args()
      sequence_len = args.seq_length
      micro_batch_size = args.micro_batch_size
      hidden_size = args.hidden_size
      global_batch_size = args.global_batch_size
      forward_backward_func = get_forward_backward_func(
          virtual_pipeline_model_parallel_size,
          pipeline_model_parallel_size,
      )
      tensor_shape = (sequence_len, micro_batch_size, hidden_size)
      for _ in range(16):
          batch = self._generate_fancy_data_labels(
              sequence_len, global_batch_size
          )
          optim.zero_grad()
          forward_backward_func(
              self._fwd_step_func,
              batch,
              model,
              forward_only=False,
              tensor_shape=tensor_shape,
              async_comm=async_comm,
              sequence_parallel_enabled=args.sequence_parallel,
          )
          # All-reduce layernorm parameters across model parallel nodes
          # when sequence parallelism is used
          if (
              parallel_state.get_tensor_model_parallel_world_size() > 1
              and args.sequence_parallel
          ):
              for model_module in model:
                  unwrapped_model = unwrap_model(model_module)
                  for param in unwrapped_model.parameters():
                      if getattr(
                          param, "sequence_parallel_enabled", False
                      ):
                          grad = param.grad
                          torch.distributed.all_reduce(
                              grad,
                              group=parallel_state.get_tensor_model_parallel_group(),
                          )
  
          optim.step()
  
imports: import torch
  import unittest
  from apex.transformer.testing import global_vars
  from apex.transformer.testing.standalone_bert import bert_model_provider
  from apex.transformer.pipeline_parallel.schedules.common import _get_params_for_weight_decay_optimization, build_model
  from apex.transformer.pipeline_parallel.schedules import get_forward_backward_func
  from apex.transformer.pipeline_parallel.utils import average_losses_across_data_parallel_group, unwrap_model, setup_microbatch_calculator
  from apex.transformer.log_util import set_logging_level
  from apex.transformer import tensor_parallel, parallel_state
  from apex.transformer.enums import ModelType
  from apex.transformer._ucc_util import HAS_UCC
  from apex.transformer.testing.distributed_test_base import UccDistributedTestBase, NcclDistributedTestBase
  import logging
  from torch.testing._internal import common_utils
label_desc: Use image node_id 4 for calling the BertTestBase obj's underlying member method code with example usage: obj._train(model, optim, virtual_pipeline_model_parallel_size, pipeline_model_parallel_size, async_comm) without return types